An Internet Traffic Classification Method Based on Semi-Supervised Support Vector Machine

Identifying and classifying different network applications is very important for trend analysis, dynamic access control, network security and traffic engineering, while traffic classification is able to classify applications effectively. Current popular methods of traffic classification mainly include machine learning algorithm based on supervised or unsupervised and the method based load. In practical applications, the above methods have high complexity or low accuracy degree, so we propose a semi-supervised support vector machine method only based on flow statistics to identify and classify network applications. In this method, SVM, "constant" flow and co-training algorithm are the key core to obtain a classifier rapidly. The classifier got by this method has three advantages contrast to the previous classical methods: 1) high classification degree; 2) high generalization performance; 3) rapid computational performance. As a proof of concept, we implement the classification algorithm based on open-resource, and show the characteristics and feasibility of our method in the campus and resident network.